Analyzing NJ COVID-19 responses with Control theory, extending a study from earlier this year.

Project Title: Analyzing NJ COVID-19 responses with Control theory, extending a study from earlier this year.
Program:
CAREERS (323)

Project Image: https://support.access-ci.org/system/files/webform/project/107/image%20for%20CAREERS.PNG
Tags:
matlab (2), programming (5), programming-best-practices (49)

Status: Complete
Project Leader
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Project Leader:
Benedetto Piccoli

Email: piccoli@camden.rutgers.edu
Mobile Phone: {Empty}
Work Phone: (856) 225-6356

Project Personnel
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Mentor(s):
Galen Collier (420), Sean McQuade (540)

Student-facilitator(s):
Ryan Weightman (544)

Mentee(s):
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Project Information
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Project Description:
The COVID-19 pandemic has infected every continent except Antarctica. Loss of life and crippling economic effects have been felt across the world. COVID-19 infection rates can be reduced through the use of testing, social distancing, and contact tracing. Each of these strategies requires a range of costs to apply. We use control theory and numerical optimization techniques with data from a variety of NJ-based sources to estimate how much each of each strategy should be applied within counties across the state in order to minimize the total cost. A student facilitator working on this project will help introduce the lab to the use of MATLAB for existing AMPL-based simulations. The student will use general computational skills and mathematical modeling techniques which will provide valuable experience with tools implemented by computational researchers today across many fields.

Information about the research associated with this project: https://rand.camden.rutgers.edu/2020/03/26/timing-county-hospital-bed-shortfall-during-covid-19/

Information about the Piccoli Lab: https://piccoli.camden.rutgers.edu/

Project Information Subsection
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Project Deliverables:
A paper will be written and published in early 2021.

Project Deliverables:
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Student Research Computing Facilitator Profile:
Advanced study in mathematics / statistical modeling
AMPL
MATLAB
Interest in computational modeling for urgent, real-world problems

Mentee Research Computing Profile:
{Empty}

Student Facilitator Programming Skill Level: Practical applications
Mentee Programming Skill Level: {Empty}
Project Institution: Rutgers-Camden
Project Address:
303 Cooper St, Camden, NJ 08102
Camden, New Jersey. 08102

Anchor Institution: CR-Rutgers
Preferred Start Date: 11/16/2020
Start as soon as possible.: No
Project Urgency: Already behind3Start date is flexible
Expected Project Duration (in months): 6
Launch Presentation: https://support.access-ci.org/system/files/webform/project/107/Ryan.Weightman-CareersPresentation.pdf
Launch Presentation Date: 02/10/2021
Wrap Presentation: {Empty}
Wrap Presentation Date: 08/11/2021
Project Milestones:
- Milestone Title: Initial feasibility study and review of work leading to this project
  Milestone Description: Initial feasibility study examining existing models and tools that may be used for this project: AMPL API for MATLAB and the MATLAB – Optimization Toolbox
  Completion Date Goal: 2021-02-10
  Actual Completion Date: 2021-02-10
- Milestone Title: NJ COVID control model paper
  Milestone Description: We will leverage simulations run by the student to complete a publication that is planned to be submitted to the journal "Networks and Heterogeneous Media" (NHM).
  Completion Date Goal: 2021-03-10
  Actual Completion Date: 2021-03-10
- Milestone Title: COVID control with Herd Immunity 
  Milestone Description: We will leverage our model with recent publications about herd immunity for a combined study
  Completion Date Goal: 2021-04-30
- Milestone Title: Expand results to US
  Milestone Description: We will do a similar study to the NJ one but consider all US
  Completion Date Goal: 2021-05-15
- Milestone Title: Model Vaccine Deployment strategy
  Milestone Description: We will study the optimal way to distribute the vaccine with computational tools.
  Completion Date Goal: 2021-05-30

Github Contributions: {Empty}
Planned Portal Contributions (if any):
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Planned Publications (if any):
A paper was submitted to Mathematical Models and Methods in Applied Sciences (M3AS) May 26, 2021.
Title: "Control of COVID-19 outbreak using an extended SEIR model."
Authors: Sean T. McQuade, Ryan Weightman, Nathaniel J. Merrill, Aayush Yadav, Emmanuel Trélat, Sarah R. Allred,  Benedetto Piccoli

What will the student learn?:
Interface between two different programming languages: AMPL and MATLAB (i.e., solving problems using the AMPL API for MATLAB).

What will the mentee learn?:
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What will the Cyberteam program learn from this project?:
They will see how their support can assist the ongoing COVID-19 struggle.

HPC resources needed to complete this project?:
{Empty}

Notes:
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Final Report
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What is the impact on the development of the principal discipline(s) of the project?:
This project builds on the standard SEIR disease spreading model. It shows that we can use control theory techniques with SEIR to estimate some key aspects of the pandemic, and evaluate the various responses used to control the spread.

What is the impact on other disciplines?:
Our project has many uses, including optimizing for the best vaccine distribution plan. This was not obvious when we started.

Is there an impact physical resources that form infrastructure?:
No.

Is there an impact on the development of human resources for research computing?:
Yes, our project shows one way to evaluate the non-pharmaceutical interventions to fight COVID-19. Similar methods will expand upon this in the future.

Is there an impact on institutional resources that form infrastructure?:
Yes, these simulations benefit from fast computing, such as using Amarel, or other RU-Camden resources. 

Is there an impact on information resources that form infrastructure?:
Yes, cluster computing may become more valuable as more tools that are similar to our simulators are implemented.

Is there an impact on technology transfer?:
No.

Is there an impact on society beyond science and technology?:
Yes, this has the capability to point us toward the good ways we halted the spread of COVID, and point out the less efficient ways as well. I expect this project, and others that are similar will help us handle the next pandemic more efficiently.

Lessons Learned:
Projects like this are based on many assumptions that are coded into simulators. Other experts in related fields are a valuable resource to make those estimates. A project such as this is ideal for getting many experts of different fields together.

Overall results:
The project was a success, not just in meeting our goals, but also in providing growth to the students, and allowing them to take the initiative to answer research questions.